Abstract
The development of Internet of Things (IoT) technology has enabled intelligent robots to have more sensing and decision-making capabilities, broadening the application areas of robots. Grasping operation is one of the basic tasks of intelligent robots, and vision-based robot grasping technology can enable robots to perform dexterous grasping. Compared with 2D images, 3D point clouds based on objects can generate more reasonable and stable grasping poses. In this paper, we propose a new algorithm structure based on the PointNet network to process object point cloud information. First, we use the T-Net network to align the point cloud to ensure its rotation invariance; then we use a multilayer perceptron to extract point cloud characteristics and use the symmetric function to get global features, while adding the point cloud characteristics attention mechanism to make the network more focused on the object local point cloud. Finally, a grasp quality evaluation network is proposed to evaluate the quality of the generated candidate grasp positions, and the grasp with the highest score is obtained. A grasping dataset is generated based on the YCB dataset to train the proposed network, which achieves excellent classification accuracy. The actual grasping experiments are carried out using the Baxter robot and compared with the existing methods; the proposed method achieves good grasping effect.